Operation state control method and system of bidirectional photovoltaic energy storage inverter

Abstract

The invention provides an operation state control method and system of a bidirectional photovoltaic energy storage inverter. Belongs to the technical field of new energy power electronic control. The method comprises the steps of obtaining initial hardware topological data and real-time working condition data of the bidirectional photovoltaic energy storage inverter, analyzing the initial hardware topological data, determining a reconfigurable hardware module of the bidirectional photovoltaic energy storage inverter, configuring the reconfigurable hardware module through a preset reconfigurable rule according to the real-time working condition data, and obtaining a dynamic hardware topological structure; constructing a real-time hardware model of the bidirectional photovoltaic energy storage inverter according to the dynamic hardware topological structure; through deep fusion of a bidirectional reconfigurable hardware topology and a multi-agent dynamic energy game, the bidirectional photovoltaic energy storage inverter can dynamically adjust a hardware structure and an energy interaction strategy according to real-time working conditions, normal form transformation from static control to dynamic self-adaption is realized, and the adaptability of the inverter to complex and changeable working conditions is greatly improved.

Description

TECHNICAL FIELD

[0001] The application provides a running state control method and system of a bidirectional photovoltaic energy storage inverter, and belongs to the technical field of new energy power electronic control. BACKGROUND

[0002] Traditional photovoltaic energy storage inverter running state control mostly adopts a static control method, that is, the inverter is controlled according to preset parameters and fixed hardware topology structure. However, in actual operation, the working conditions of the photovoltaic system are complex and changeable, such as light intensity, temperature, load demand and other factors, and the static control method is difficult to flexibly adjust according to real-time working conditions, resulting in low running efficiency of the inverter, large energy loss, and inability to fully exert the performance advantages of the photovoltaic energy storage system. Therefore, a bidirectional photovoltaic energy storage inverter control method capable of dynamically adjusting the running state according to real-time working conditions is needed. SUMMARY

[0003] The application provides a running state control method and system of a bidirectional photovoltaic energy storage inverter, which solves the problems mentioned in the background.

[0004] The application provides a running state control method of a bidirectional photovoltaic energy storage inverter, which comprises the following steps:

[0005] S1: obtaining initial hardware topology data and real-time working condition data of the bidirectional photovoltaic energy storage inverter, analyzing the initial hardware topology data to determine the reconfigurable hardware modules thereof, configuring the reconfigurable hardware modules according to the real-time working condition data through a preset reconfigurable rule to obtain a dynamic hardware topology structure, and constructing a real-time hardware model of the bidirectional photovoltaic energy storage inverter according to the dynamic hardware topology structure;

[0006] S2: dividing the bidirectional photovoltaic energy storage inverter system into multiple agents, respectively extracting energy characteristics of each agent, constructing an energy game model of each agent according to the extracted energy characteristics, solving the energy game model of each agent through a multi-agent dynamic energy game algorithm, and obtaining energy game decision results of each agent;

[0007] S3: based on the real-time hardware model obtained in S1 and the energy game decision results of each agent obtained in S2, simulating the energy flow of the bidirectional photovoltaic energy storage inverter, and obtaining device energy transmission simulation data of the bidirectional photovoltaic energy storage inverter under different working conditions;

[0008] S4: Obtain the operation demand data of the bidirectional photovoltaic energy storage inverter, evaluate the operation demand data according to the energy game decision result of each intelligent agent in S2 and the equipment energy transmission simulation data in S3, analyze whether the current operation state meets each demand, if not, calculate the energy adjustment amount required to meet the operation demand, and obtain operation demand energy evaluation data;

[0009] S5: According to the equipment energy transmission simulation data in S3 and the operation demand energy evaluation data in S4, determine the operation state adjustment strategy of the bidirectional photovoltaic energy storage inverter; combined with the reconfigurable characteristics of the dynamic hardware topology structure in S1, further optimize the configuration of the hardware structure according to the operation state adjustment strategy, and according to the result of the multi-agent dynamic energy game, fine-tune the energy interaction strategy of each intelligent agent, and perform the cooperative optimization of hardware structure evolution and intelligent energy flow game decision; the operation state control strategy after the cooperative optimization is transmitted to the control unit of the bidirectional photovoltaic energy storage inverter, and the operation state control task is executed, realizing the dynamic self-adaptive adjustment of the inverter operation state.

[0010] The application provides a bidirectional photovoltaic energy storage inverter operation state control system, which comprises:

[0011] One or more processors;

[0012] A memory for storing one or more programs,

[0013] When the one or more programs are executed by the one or more processors, the one or more processors realize the method in any one of the above.

[0014] The application has the advantages that: through the deep integration of the bidirectional reconfigurable hardware topology and the multi-agent dynamic energy game, the bidirectional photovoltaic energy storage inverter can dynamically adjust the hardware structure and the energy interaction strategy according to the real-time working condition, realizes the paradigm change from static control to dynamic self-adaptation, and greatly improves the adaptability of the inverter to complex and variable working conditions.

[0015] The real-time evolution of the hardware structure and the intelligent game decision of the energy flow can optimize the energy transmission path and the distribution mode, reduce energy loss, and improve the overall operation efficiency of the photovoltaic energy storage inverter.

[0016] The multi-agent dynamic energy game considers the interests and constraint conditions of each intelligent agent, can balance the energy relationship among the photovoltaic array, the energy storage battery and the load, avoid the system instability problem caused by the imbalance between energy supply and demand, and enhance the stability of the entire photovoltaic energy storage system.

[0017] Through reasonable energy management and hardware structure optimization, the number of charge and discharge times and the depth of the energy storage battery can be reduced, the working stress of the hardware module is reduced, and thus the service life of the bidirectional photovoltaic energy storage inverter and related equipment is prolonged. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 The method is described in the present application. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present application are described below with reference to the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application.

[0020] One embodiment of the present application is shown in Figure 1 A running state control method of a bidirectional photovoltaic energy storage inverter, the method comprising:

[0021] S1: obtaining initial hardware topology data of the bidirectional photovoltaic energy storage inverter and real-time working condition data, the real-time working condition data including light intensity, temperature and load demand information; analyzing the initial hardware topology data to determine the reconfigurable hardware modules thereof, the hardware modules including power switching tubes, inductors and capacitors; configuring the reconfigurable hardware modules according to the real-time working condition data through a preset reconfigurable rule, performing real-time evolution of the hardware structure, including multi-level / multi-port / redundancy mode switching, to obtain a dynamic hardware topology structure; and constructing a real-time hardware model of the bidirectional photovoltaic energy storage inverter according to the dynamic hardware topology structure;

[0022] S2: dividing the bidirectional photovoltaic energy storage inverter system into multiple agents, the agents including a photovoltaic array agent, an energy storage battery agent and a load agent; and respectively extracting energy characteristics of each agent, for the photovoltaic array agent, extracting its output power, voltage, current and other characteristics; for the energy storage battery agent, extracting its state of charge, charge and discharge power and other characteristics; for the load agent, extracting its power demand, power consumption mode and other characteristics; constructing an energy game model of each agent according to the extracted energy characteristics, the model considering energy benefits, costs and constraint conditions of each agent; solving the energy game model of each agent through a multi-agent dynamic energy game algorithm to obtain an energy game decision result of each agent, i.e., the optimal strategy of each agent in energy interaction;

[0023] S3: Based on the real-time hardware model obtained in S1 and the energy game decision results of each agent obtained in S2, simulate the energy flow of the bidirectional photovoltaic energy storage inverter; in the simulation process, consider the influence of the dynamic change of the hardware structure on the energy transmission and the effect of the energy game decision of each agent on the energy distribution; obtain the device energy transmission simulation data of the bidirectional photovoltaic energy storage inverter under different working conditions, the transmission simulation data including the power, voltage and current of each port and other parameters;

[0024] S4: Obtain the operation requirement data of the bidirectional photovoltaic energy storage inverter, the operation requirement data including system efficiency requirement, power quality requirement and energy storage battery life protection requirement; according to the energy game decision results of each agent in S2 and the device energy transmission simulation data in S3, evaluate the operation requirement data and analyze whether the current operation state meets the requirements; if not, calculate the energy adjustment amount required to meet the operation requirements and obtain the operation requirement energy evaluation data;

[0025] S5: According to the device energy transmission simulation data in S3 and the operation requirement energy evaluation data in S4, determine the operation state adjustment strategy of the bidirectional photovoltaic energy storage inverter; combined with the reconfigurable characteristics of the dynamic hardware topology structure in S1, further optimize the configuration of the hardware structure according to the operation state adjustment strategy, the optimization configuration including adjusting the number of multi-level and the connection mode of multi-port; at the same time, according to the results of multi-agent dynamic energy game, fine-tune the energy interaction strategy of each agent, and perform cooperative optimization of hardware structure evolution and energy flow intelligent game decision; transmit the cooperative optimization operation state control strategy to the control unit of the bidirectional photovoltaic energy storage inverter, execute the operation state control task, and realize the dynamic self-adaptive adjustment of the inverter operation state.

[0026] The working principle and effect of the above technical scheme are as follows: by dynamically adjusting the hardware topology structure, stable operation can be maintained when the conditions such as illumination, temperature and load change, and the adaptability of the bidirectional photovoltaic energy storage inverter to complex working conditions is improved; the multi-agent energy game model makes the energy interaction among photovoltaic, energy storage and load more efficient, reduces energy waste and improves the rationality of system energy distribution; the cooperative optimization of hardware structure and energy strategy reduces unnecessary power loss and improves system operation efficiency;

[0027] By precisely controlling the charging and discharging process and protection strategy, the service life of the battery is prolonged; the waste speed of the energy storage battery is reduced, the switching and dynamic adjustment mechanism of the redundant mode reduces the failure caused by hardware overload or parameter imbalance; the probability of system failure is reduced;

[0028] The switching of the multi-level and multi-port mode can meet the energy transmission demand in different scenes, enhances the stability of power quality, accurately regulates parameters such as voltage and current, reduces waveform distortion and fluctuation, ensures that the load can obtain stable power supply under various working conditions, reduces the risk of power interruption, and enhances the reliability of user power consumption.

[0029] In one embodiment of the present application, the S1 comprises:

[0030] S11, initial hardware topology data of the bidirectional photovoltaic energy storage inverter is acquired, the initial hardware topology data comprises static attribute data, the static attribute data comprises power conversion unit circuit structure, component parameter specification and module connection relationship; real-time working condition data is synchronously collected, the real-time working condition data comprises dynamic parameters, the dynamic parameters comprise illumination intensity (resolution is not less than 10 W / m 2 ), environmental temperature (accuracy ±0.5 DEG C), load demand (containing instantaneous power, average power and fluctuation frequency), grid voltage / frequency; the collected data is preprocessed, and a standardized data set is constructed;

[0031] S12, based on the initial hardware topology data, a modular decomposition algorithm is used, and a reconfigurable hardware module is identified in combination with graph theory and circuit topology analysis, the reconfigurable hardware module can be specifically divided into: a power switch tube module (containing IGBT / MOSFET and a driving circuit), an inductor module (containing a magnetic core material and a winding parameter), a capacitor module (containing a capacitance and a voltage withstand grade), and an interface conversion module; a reconfigurable capability evaluation matrix of each module is established, and a supported reconfiguration mode is labeled, the reconfiguration mode comprises series / parallel switching and a parameter adjustable range;

[0032] S13, according to the preprocessed real-time working condition data, a preset reconfigurable rule library is called, the reconfigurable rule library comprises illumination-load matching rules and temperature-power limitation rules, dynamic configuration is performed on the reconfigurable hardware module, the dynamic configuration comprises starting a multi-level mode (improving an output voltage grade) in high-illumination high-load, switching a multi-port mode (expanding an energy interaction channel) in multi-source access, and activating a redundancy mode (switching a standby module) in component fault early warning; real-time switching between modules is performed through a hardware configuration instruction set, and a dynamic hardware topology structure is generated;

[0033] S14, based on the dynamic hardware topology structure, in combination with real-time parameters of each module, the real-time parameters of each module comprise switch tube on-resistance and inductor equivalent impedance, a bidirectional photovoltaic energy storage inverter real-time hardware model based on a state space equation is constructed; least square method is used to calibrate the model parameters online, and a high-precision model directly used for simulation analysis is formed.

[0034] The working principle and effects of the above technical solutions are as follows: through accurate identification and dynamic configuration of the reconfigurable module, the same set of hardware can adapt to different working conditions, reducing equipment idling and improving the utilization efficiency of the hardware topology; high-precision collection and standardized processing of dynamic parameters such as light and temperature provide a reliable basis for subsequent hardware configuration, improving the accuracy of data collection; the real-time hardware model constructed based on the state space equation can more truly reflect the device running state after online calibration, providing strong support for system optimization and improving the accuracy of model simulation;

[0035] Through flexible reconfiguration of the module to replace part of the new hardware requirements, the equipment procurement and replacement costs are reduced, and the hardware investment cost is reduced; dynamic configuration makes the hardware always work in a state matching the current demand, reducing the invalid energy consumption and reducing the energy consumption caused by improper working condition adaptation; the preset rule library automatically drives the module switching, reducing the frequency of manual adjustment and the error probability, and reducing the workload of manual configuration;

[0036] Flexible switching of multi-level, multi-port and other modes can cope with various situations such as sudden changes in light and load fluctuations, enhancing the system's ability to adapt to complex environments; the same module realizes multiple functions through reconfiguration modes such as series / parallel connection, improving the comprehensive utilization value of the equipment and enhancing the reusability of the hardware module; the redundant mode switches to the standby module in time when fault warning occurs, reducing the risk of system interruption caused by single-point failure and enhancing the stability of system operation.

[0037] In an embodiment of the present application, the S2 comprises:

[0038] S21, a distributed intelligent agent modeling method is adopted to divide the bidirectional photovoltaic energy storage inverter system into three types of core intelligent agents, including a photovoltaic array intelligent agent, an energy storage battery intelligent agent and a load intelligent agent; the photovoltaic array intelligent agent is used for light energy capture and conversion; the energy storage battery intelligent agent is used for energy storage and release; the load intelligent agent is used for energy consumption and demand response; and the communication boundary (using the communication protocol of IEEE1519 standard) and the energy interaction range (defining the power threshold of the input / output port) of each intelligent agent are determined;

[0039] S22, for the photovoltaic array agent, the first characteristic parameter is collected and extracted by the synchronous phasor measurement unit, and the first characteristic parameter includes but is not limited to power fluctuation rate, voltage distortion rate, maximum tracking point (MPPT) offset; for the energy storage battery agent, the second characteristic parameter is obtained by using Kalman filtering algorithm, and the second characteristic parameter includes but is not limited to state of charge (SOC), state of health (SOH), charge and discharge efficiency curve and cycle life attenuation rate; for the load agent, the third characteristic parameter is extracted by load characteristic analysis, and the third characteristic parameter includes but is not limited to power demand peak valley difference, power mode label (such as resistance / inductance) and demand response sensitivity; a characteristic parameter time sequence database is constructed;

[0040] S23, based on the non-cooperative game theory, an energy game model of each agent is constructed; the energy game model includes: taking the maximum generation income of the photovoltaic array agent as the objective function, taking the minimum charge and discharge cost of the energy storage battery agent as the objective function, and taking the maximum power consumption satisfaction of the load agent as the objective function; the constraint conditions are introduced, including the upper limit of photovoltaic power output, the SOC safety interval of battery (usually 20%-80%), and the reliability requirement of load power supply (power supply interruption time≤50ms); a game equilibrium equation of multi-objective optimization is established;

[0041] S24, the improved particle swarm optimization algorithm is used to solve the energy game model, the strategy space of each agent is initialized, the strategy space includes photovoltaic output adjustment step, battery charge and discharge power gear and load demand adjustment coefficient, and the Nash equilibrium point is found through iterative calculation; the optimal strategy set of each agent is output, the optimal strategy set includes real-time output plan of photovoltaic array, charge and discharge time sequence of energy storage battery and demand response scheme of load, and the energy game decision result is formed.

[0042] The working principle and effect of the above technical scheme are as follows: the system is divided into different agents and a game model is constructed, so that the energy cooperation between photovoltaic, energy storage and load is more in line with actual demand, the waste caused by energy mismatch is reduced, and the rationality of energy distribution is improved; special collection and processing methods are used for different agents, such as Kalman filtering algorithm for battery state analysis, so that the parameters can better reflect the real state, providing reliable basis for decision-making, and improving the accuracy of characteristic parameter extraction; the improved particle swarm optimization algorithm can quickly find the equilibrium point, the optimal strategy set output makes each part run more coordinately, the overall energy efficiency is higher, and the strategy optimization efficiency is improved.

[0043] The game model is reasonably planned for charging and discharging time sequence, overcharging and overdischarging are avoided, the battery aging speed is slowed down, and the loss of the energy storage battery is reduced;The clear demand response scheme can adjust the power consumption according to the actual situation, reduces the situation of high and low power supply, and reduces the fluctuation of load power supply;The autonomous interaction of the agent and the automatic solution of the algorithm replace part of the manual adjustment, reduce the labor input and operation error, and reduce the cost of artificial intervention;

[0044] The agents cooperate in the clear communication boundary and interaction range, like gears, and run smoothly, enhancing the coordination of the system;The multi-objective optimization game model can find the optimal solution while meeting various constraint conditions, so that the system can also run stably under changing light and fluctuating load, enhancing the ability to cope with complex working conditions;The demand response scheme of the load agent can better adapt to the power consumption mode, reducing the disturbance caused by power supply interruption or instability, and enhancing the user's satisfaction with power consumption.

[0045] In an embodiment of the present application, the S24 comprises:

[0046] The energy game model constructed based on S23 determines the strategy space boundary of each agent, specifically including: the output adjustment step range of the photovoltaic array agent (0-5% rated power / step), the charging and discharging power gear of the energy storage battery agent (5 gears, each gear corresponding to 20% rated power), and the demand adjustment coefficient interval of the load agent (0.8-1.2);Initialize the parameters of the improved particle swarm optimization algorithm, including the particle population size (50-100), the maximum number of iterations (500 times), the initial inertia weight (0.9), the learning factor (c1=c2=2.0), and generate the initial particle swarm (each particle corresponds to a set of agent strategy combination);

[0047] For each particle (strategy combination) in the initial particle swarm, substitute the objective function and constraint condition of the energy game model, calculate the quantitative value of photovoltaic array power generation income, energy storage charging and discharging cost, and load power consumption satisfaction;Check whether the constraints such as photovoltaic power upper limit, battery SOC interval, and load power supply reliability are met (particles that do not meet the constraints are given a penalty fitness);The weighted summation method (weights are set according to the importance of the target) is used to convert the multi-objective function into a single-objective fitness value, forming a particle fitness evaluation set;

[0048] Based on the particle fitness evaluation set, the iterative process of the improved particle swarm optimization algorithm is performed, the self-adaptive inertia weight (linearly decreasing from 0.9 to 0.4 with the number of iterations) is introduced to balance the global exploration and local development ability; according to the historical optimal position of each particle and the global optimal position of the population, the particle speed and position are updated (strategy combination), and it is ensured that the new position falls within the boundary of the strategy space; a mutation operation (random disturbance of 10% of the particle position) is performed every 50 iterations to avoid falling into local optimum and generate an updated particle swarm and corresponding fitness set;

[0049] During the iteration process, the change of the optimal fitness value of the population is monitored in real time, and when the optimal fitness value of 30 consecutive iterations fluctuates by ≤0.1%, it is determined that the algorithm converges, and the optimal position of the population at this time corresponds to the Nash equilibrium point; if it does not converge and reaches the maximum number of iterations, a local search strategy (fine search around the current optimal position) is used for further optimization until the convergence condition is met, and the final Nash equilibrium point corresponding to the strategy combination is determined;

[0050] From the strategy combination corresponding to the Nash equilibrium point, the specific strategies of each agent are extracted: the real-time output plan of the photovoltaic array agent (including the power set value and MPPT adjustment instruction every 15 minutes), the charging and discharging time sequence of the energy storage battery agent (including the charging and discharging start / end time and power gear switching node), and the demand response scheme of the load agent (including the power reduction ratio and time window of adjustable load); these strategies are integrated into a structured optimal strategy set, and after strategy effectiveness verification (whether it meets all constraints and objectives by substituting into the model), the final energy game decision result is formed.

[0051] The working principle and effect of the above technical solution are: the strategy space boundary of each agent is clear, the optimization process has a clear range, combined with the iterative optimization of the improved particle swarm algorithm, the Nash equilibrium point found is more in line with the actual operation demand, improving the accuracy of strategy solving; the combination of self-adaptive inertia weight and periodic mutation operation avoids blind search, makes the optimal solution appear faster, reduces unnecessary calculation time, and improves the convergence speed of the algorithm; the specific strategies extracted finally are verified for effectiveness to ensure that they can meet all constraint conditions, making the execution more reliable and improving the feasibility of the strategies.

[0052] The strategies of each agent are coordinated with each other, avoiding problems such as waste of photovoltaic output and ineffective charging and discharging of batteries, reducing system energy loss and reducing the irrationality of energy distribution; the whole process from strategy space setting to final verification is controlled, making the output optimal strategy set more reliable, reducing operation failures caused by decision deviation, and reducing the probability of decision errors; the algorithm automatically generates detailed output plans, charging and discharging time sequences, etc., replacing part of manual operations, saving labor costs and reducing the frequency of manual adjustments.

[0053] The photovoltaic, energy storage, and load strategies are matched with each other, operate like a set of precise processes, improve the fluency of the overall system, and enhance the coordination of the system operation; the strategy space covers different adjustment ranges, plus the dynamic optimization of the algorithm, can adapt to the changes of conditions such as light and load, maintain the stability of the system, and enhance the flexibility of dealing with variables; the structured optimal strategy set contains specific time nodes, power values and the like, so that the operation logic of each agent can be clearly understood, subsequent analysis and adjustment are facilitated, and the explainability of the decision result is enhanced.

[0054] In an embodiment of the present application, the S3 comprises:

[0055] S31, integrate the real-time hardware model parameters (for example, module switching response time, power loss coefficient) of S14 and the decision results (for example, the energy interaction instructions of each agent) of S24, build a simulation input system containing 58 parameters; set the simulation time step and the working condition scene label, the simulation time step is not greater than 100 ms; the working condition scene label includes sunny day / overcast day, peak / valley load;

[0056] S32, build a coupling simulation platform of hardware structure and energy flow based on MATLAB / Simulink, call the dynamic hardware topology structure model in real time during simulation, calculate the influence of hardware module switching on energy transmission path, including power loss caused by impedance change); substitute the energy game decision of each agent, simulate the dynamic flow process of energy among photovoltaic-energy storage-load-grid; introduce random disturbance factors, including light mutation and load impact, simulate the system response under extreme working conditions;

[0057] S33, collect key parameters during simulation, including real-time power (accuracy ±1%), voltage effective value (±0.5%), current waveform distortion rate (THD), energy conversion efficiency, etc. of each port; segment the collected data in time sequence (divide according to working condition change points), calculate the statistical characteristics of each period, including mean, variance and peak value; build a device energy transmission simulation database, associate the corresponding hardware topology state and game decision parameters.

[0058] The working principle and effect of the above technical solution are as follows: the hardware model parameters and the game decision results are integrated, the input system of 58 parameters makes the simulation scene closer to the actual operation situation, avoids the omission of key factors, and improves the comprehensiveness of energy flow simulation; the high-precision collection of parameters such as power and voltage of each port, plus the statistical analysis after time sequence segmentation, makes the simulation result more valuable, improves the accuracy of data collection; the introduction of random disturbance factors such as light mutation and load impact can find potential problems of the system under special conditions in advance, provide direction for subsequent optimization, and improve the response ability under extreme working conditions.

[0059] The simulation platform built by MATLAB / Simulink can simulate various working conditions in a laboratory environment, reduce the number of on-site debugging and resource investment, and reduce the cost of field testing; the power loss and energy transmission bottleneck caused by hardware switching are exposed in advance during simulation, avoiding possible failures in actual operation and reducing the risk of system operation; the device energy transmission simulation database associates hardware state, game decision and transmission parameters, making subsequent query and analysis more convenient, reducing data processing time and reducing data association difficulty;

[0060] The coupling simulation platform can intuitively reflect the influence of hardware changes on energy transmission by calling the dynamic hardware topology model in real time, making the cooperation of the two more coordinated, and enhancing the synergy of hardware and energy flow; simulation covering various scene tags such as sunny / cloudy days, peak / valley loads, etc. enables the system to find the appropriate operation mode in different environments, improving the adaptability of the system; the strategy based on detailed simulation data is more reliable than relying solely on experience, improving the stability and efficiency of system operation.

[0061] In one embodiment of the present application, the S33 comprises:

[0062] Through the real-time monitoring module of the coupling simulation platform, key parameters in the simulation process are collected, including real-time power (accuracy ±1%), voltage effective value (±0.5%), current waveform distortion rate (THD), energy conversion efficiency of each port, and state parameters such as temperature and ripple of hardware modules, forming an original simulation parameter set.

[0063] According to the working condition change characteristics in the original simulation parameter set, the working condition change characteristics include power mutation and temperature rise, the collected data is time series segmented according to the working condition change points, different stable working condition intervals and transition intervals are divided, and a segmented time series data set is obtained.

[0064] For each interval in the segmented time series data set, the statistical characteristics of each period are calculated, and a feature index set reflecting the data characteristics of the interval is extracted;

[0065] The feature index set is associated and matched with the corresponding hardware topology state data and game decision parameters, and a device energy transmission simulation database is constructed by establishing an indexing mechanism for data tracing and query.

[0066] The working principle and effects of the above technical solution are as follows: key parameters such as power, voltage, and temperature are comprehensively collected, and even details such as hardware ripple are not overlooked, so that the original data can fully reflect the system operation state, and the integrity of the simulation data is improved; according to the working condition characteristics such as power mutation and temperature rise, the stable interval and the transition interval are divided for separate processing, so that the data analysis of each interval is more focused, and the pertinence of the data is improved; through the index mechanism, the characteristic index is associated with the hardware state and the game decision, so that any part of the data can be quickly located without searching in the massive information, and the efficiency of data query is improved;

[0067] After the segmented processing, irrelevant interference information is eliminated, so that the subsequent analysis can focus on valuable data, and the interference of invalid data is reduced; the database integrates scattered parameters, states, and decisions together, which saves the trouble of manual comparison, reduces the possibility of errors, and reduces the complexity of data association; based on the characteristic index set, the problems under different working conditions can be clearly seen, the optimization direction is more clear, the blind operation is avoided, and the blindness of system optimization is reduced;

[0068] From the original parameters to the final decision, each step can be traced down, and the root cause of any problem can be quickly found, so that the traceability of the data is enhanced; the key nodes such as power mutation and temperature rise are specially captured, so that the system can respond to abnormal conditions more quickly, and the sensitivity to working condition changes is enhanced; the characteristic index of each interval can dig out the specific operation law, and provide a solid basis for subsequent system upgrade.

[0069] In an embodiment of the present application, the S4 comprises:

[0070] S41, obtaining the operation demand data of the bidirectional photovoltaic energy storage inverter through the system control center, specifically including: system efficiency requirement (weighted average efficiency ≥ 95%), power quality requirement (voltage deviation ≤ ± 5%, frequency deviation ≤ ± 0.2 Hz, THD ≤ 5%), energy storage battery life protection requirement (single charge-discharge depth ≤ 80%, cycle number attenuation coefficient ≤ 0.01 / time), and grid-connected requirement (satisfying the fault ride-through capability of IEEE1547 standard); the demand data is quantified into an evaluable index threshold value;

[0071] S42, constructing an operation state evaluation model based on the energy game decision result of S24 and the transmission simulation data of S33: the weights of the demand indexes are determined by using the analytic hierarchy process, and the weight of each index is specifically 30% for efficiency, 25% for power quality, 25% for battery life, and 20% for grid-connected requirement; a deviation function of the index actual value and the threshold value is established, and the single-index compliance rate and the comprehensive compliance index are calculated;

[0072] S43, substitute the analog data into the evaluation model, calculate the comprehensive compliance index under the current running state: if the comprehensive compliance index is greater than or equal to 0.9 (set threshold value), it is determined that the running demand is met; if it is less than 0.9, the non-compliance index (such as insufficient efficiency, THD exceeding the standard, etc.) is located, and the correlation with the hardware topology structure and the energy game strategy is analyzed;

[0073] S44, for the non-compliance running demand, based on the deviation function, the required energy adjustment amount is backstepped, for the insufficient efficiency problem, the power distribution correction value required to improve the efficiency is calculated; for the power quality problem, the compensation current amplitude and phase required to suppress the harmonic are calculated; for the battery life problem, the power limit value of optimizing the charging and discharging depth is calculated; and each adjustment amount is integrated into the running demand energy evaluation data.

[0074] The working principle and effect of the above technical scheme are: the efficiency, power quality and other requirements are quantified into specific threshold values, and the analytic hierarchy process is used to determine the weight of each index, so that the evaluation result is more in line with the core demand of actual operation, and the accuracy of the running demand evaluation is improved; the comprehensive compliance index is used to quickly judge whether the system meets the demand, and once it does not meet the demand, the specific problem can be directly locked, the time for blind troubleshooting is saved, and the efficiency of problem positioning is improved; different non-compliance items are calculated for special adjustment amount, such as insufficient efficiency to correct power distribution, harmonic exceeding the standard to calculate compensation current, so that the optimization measures are more accurate, and the pertinence of energy adjustment is improved;

[0075] The hard requirements such as battery life protection and grid connection are evaluated in advance, so as to avoid equipment damage or safety problems caused by excessive charging and discharging and non-compliance grid connection, and reduce the risk of system operation; through power distribution correction, charging and discharging depth limitation and other adjustments, invalid energy consumption and unnecessary energy loss are reduced; data models and quantitative indicators are used to replace experience judgment, so that the evaluation result is more objective, and human error is reduced.

[0076] Strictly in accordance with the standard requirements of IEEE1547 and other standards, it is ensured that the grid connection operation meets the industry specifications, and the risk of punishment due to non-compliance is avoided; by accurately controlling the battery charging and discharging depth and the number of cycles, the battery aging speed is slowed down, and the service life is prolonged; stable voltage, qualified frequency and low harmonic content make the electrical equipment run more stably, and reduce the failure caused by power quality problems.

[0077] An embodiment of the present application, the S5, comprises:

[0078] S51, based on the device energy transmission simulation data of S33 and the operating demand energy evaluation data of S44, an initial adjustment strategy is generated by using a fuzzy control algorithm; the initial adjustment strategy includes an explicit hardware structure adjustment direction (such as increasing the number of levels, switching port connection) and an energy interaction strategy correction amount (such as photovoltaic output fine tuning value, battery charging and discharging power correction coefficient); a plurality of groups of alternative strategy schemes (not less than 3 groups) are formulated;

[0079] S52, in combination with the dynamic hardware topology reconfigurable characteristic of S13, the alternative strategies are executed for cooperative optimization, and a genetic algorithm is used for joint optimization of hardware structure parameters and energy interaction strategies; the hardware structure parameters include the number of levels and the port connection mode; the energy interaction strategies include the power distribution ratio of each intelligent agent, and the target function is set to maximize the comprehensive compliance index; and a constraint condition is introduced to ensure the feasibility of hardware switching (such as switching time ≤10 ms) and the safety of energy adjustment (such as not exceeding the rated power of the device); and an optimal cooperative optimization strategy is output;

[0080] S53, the optimal cooperative optimization strategy is simulated and verified on a real-time hardware model, the hardware state change and energy flow response after the strategy is executed are simulated, and whether all operating requirements are met (comprehensive compliance index ≥0.95) is verified; potential risks (such as hardware overcurrent, voltage surge) are analyzed and warned, and if there is a risk, the strategy is returned to S51 to be regenerated;

[0081] S54, the cooperative optimization strategy that passes the verification is converted into standardized control instructions (in accordance with the Modbus-RTU protocol), and is transmitted to the control unit of the bidirectional photovoltaic energy storage inverter through industrial Ethernet; after the control unit analyzes the instructions, the hardware module is driven to perform a reconfiguration operation, and the reconfiguration operation includes on / off timing adjustment of the power switch tube, and energy interaction correction instructions are issued to each intelligent agent; the execution effect of the strategy is monitored in real time, a closed-loop control is formed, and dynamic adaptive adjustment of the operating state of the inverter is realized.

[0082] The working principle and effect of the above technical solution are as follows: the initial strategy is generated in combination with simulation data and evaluation results, and the hardware and energy strategies are jointly optimized by using a genetic algorithm, so that the adjustment scheme not only fits the actual working condition but also maximizes the comprehensive benefit, thereby improving the scientificity of the operating state adjustment; the simulation verification link strictly checks, ensures that the optimization strategy can meet all operating requirements, and can also early warn potential risks, thereby reducing the possibility of problems in actual execution; the hardware switching time is controlled within 10 ms, and the closed-loop monitoring is adjusted in real time, so that the inverter can quickly adapt to the working condition change and maintain stable operation, thereby improving the timeliness of the system response;

[0083] The optimal hardware configuration scheme is found through fuzzy control and genetic algorithm, component loss caused by blind adjustment is avoided, hardware service life is prolonged, hardware reconstruction cost is reduced, precise power distribution ratio and charge-discharge correction coefficient reduce unnecessary energy conversion loss, improve overall energy efficiency, and reduce energy regulation loss; From strategy generation to execution, the whole process is automated, combined with closed-loop control automatic correction deviation, reducing the workload and failure rate of manual operation, reducing the frequency of manual intervention;

[0084] The dynamic hardware topology combined with the cooperative optimization of energy strategy can flexibly cope with various changes such as light and load, and can find the appropriate operation mode regardless of the working condition, thereby enhancing the self-adaptive ability of the system; Strictly follow the constraints such as power limit and overcurrent warning to avoid equipment damage or safety accidents caused by hardware overload and abnormal voltage, and enhance the safety of equipment operation; Hardware reconstruction and energy interaction adjustment are synchronously promoted, and the cooperation between intelligent agents and hardware modules is more tacit, so that the whole system can efficiently operate like an organic whole.

[0085] In an embodiment of the present application, a running state control system of a bidirectional photovoltaic energy storage inverter comprises:

[0086] One or more processors;

[0087] A memory for storing one or more programs,

[0088] When the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of the above.

[0089] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A method for controlling the operating state of a bidirectional photovoltaic energy storage inverter, characterized in that, The method comprises: S1: obtaining initial hardware topology data and real-time working condition data of the bidirectional photovoltaic energy storage inverter, analyzing the initial hardware topology data to determine the reconfigurable hardware modules, configuring the reconfigurable hardware modules according to the real-time working condition data through the preset reconfigurable rules to obtain a dynamic hardware topology structure, and constructing a real-time hardware model of the bidirectional photovoltaic energy storage inverter according to the dynamic hardware topology structure; S2: dividing the bidirectional photovoltaic energy storage inverter system into multiple agents, extracting energy characteristics of each agent, constructing an energy game model of each agent according to the extracted energy characteristics, solving the energy game model of each agent through a multi-agent dynamic energy game algorithm, and obtaining energy game decision results of each agent; S3: based on the real-time hardware model obtained in S1 and the energy game decision results of each agent obtained in S2, simulating energy flow of the bidirectional photovoltaic energy storage inverter, and obtaining device energy transmission simulation data of the bidirectional photovoltaic energy storage inverter under different working conditions; S4: obtaining operation requirement data of the bidirectional photovoltaic energy storage inverter, evaluating the operation requirement data according to the energy game decision results of each agent in S2 and the device energy transmission simulation data in S3, analyzing whether the current operation state meets the requirements, calculating the energy adjustment amount required to meet the operation requirements if the requirements are not met, and obtaining operation requirement energy evaluation data; S5: determining an operation state adjustment strategy of the bidirectional photovoltaic energy storage inverter according to the device energy transmission simulation data in S3 and the operation requirement energy evaluation data in S4, further optimizing and configuring the hardware structure according to the operation state adjustment strategy and the reconfigurable characteristics of the dynamic hardware topology structure in S1, fine-tuning the energy interaction strategy of each agent according to the results of the multi-agent dynamic energy game, and performing collaborative optimization of hardware structure evolution and energy flow intelligent game decision; transmitting the collaborative optimized operation state control strategy to a control unit of the bidirectional photovoltaic energy storage inverter to perform an operation state control task, and realizing dynamic self-adaptive adjustment of the inverter operation state.

2. The method for controlling the operating state of a bidirectional photovoltaic energy storage inverter according to claim 1, characterized in that, The S1 comprises: S11, obtaining initial hardware topology data of the bidirectional photovoltaic energy storage inverter, synchronously collecting real-time working condition data, preprocessing the collected data, and constructing a standardized data set; S12, based on the initial hardware topology data, identifying the reconfigurable hardware modules through a modular decomposition algorithm, combining graph theory and circuit topology analysis, establishing a reconfigurable capability evaluation matrix of each module, and labeling the supported reconfiguration modes; S13, according to the preprocessed real-time working condition data, calling a preset reconfigurable rule library, executing dynamic configuration on the reconfigurable hardware modules, performing real-time switching between modules through a hardware configuration instruction set, and generating a dynamic hardware topology structure; S14, based on the dynamic hardware topology structure, combining real-time parameters of each module, constructing a bidirectional photovoltaic energy storage inverter real-time hardware model based on a state space equation; using the least square method to calibrate the model parameters online to form a high-precision model that can be directly used for simulation analysis.

3. The method for controlling the operating state of a bidirectional photovoltaic energy storage inverter according to claim 2, characterized in that, The initial hardware topology data includes static attribute data, and the static attribute data includes power conversion unit circuit structure, component parameter specification, and module connection relationship. The real-time working condition data includes dynamic parameters, and the dynamic parameters include illumination intensity, environmental temperature, load demand, and grid voltage / frequency.

4. The method for controlling the operating state of a bidirectional photovoltaic energy storage inverter according to claim 1, characterized in that, The S2 includes: S21, adopting a distributed intelligent agent modeling method, dividing the bidirectional photovoltaic energy storage inverter system into three types of core intelligent agents, and determining the communication boundary and energy interaction range of each intelligent agent; S22, for the photovoltaic array intelligent agent, collecting and extracting output first feature parameters through a synchronous phasor measurement unit, for the energy storage battery intelligent agent, acquiring second feature parameters by using a Kalman filtering algorithm, and for the load intelligent agent, extracting third feature parameters through load characteristic analysis, and constructing a feature parameter time sequence database; S23, based on a non-cooperative game theory, constructing an energy game model of each intelligent agent; introducing a constraint condition, and establishing a multi-objective optimization game equilibrium equation; S24, solving the energy game model by using an improved particle swarm optimization algorithm: initializing the strategy space of each intelligent agent, finding a Nash equilibrium point through iterative calculation; outputting an optimal strategy set of each intelligent agent, and forming an energy game decision result.

5. The method for controlling the operating state of a bidirectional photovoltaic energy storage inverter according to claim 4, characterized in that, The three types of core intelligent agents include a photovoltaic array intelligent agent, an energy storage battery intelligent agent, and a load intelligent agent; the photovoltaic array intelligent agent is used for light energy capture and conversion; the energy storage battery intelligent agent is used for energy storage and release; and the load intelligent agent is used for energy consumption and demand response.

6. The method for controlling the operating state of a bidirectional photovoltaic energy storage inverter according to claim 4, characterized in that, The first feature parameters include, but are not limited to, power fluctuation rate, voltage distortion rate, and maximum tracking point offset; The second feature parameters include, but are not limited to, state of charge, health state, charge-discharge efficiency curve, and cycle life attenuation rate. The second feature parameters include, but are not limited to, state of charge, health state, charge-discharge efficiency curve, and cycle life attenuation rate.

7. The method for controlling the operating state of a bidirectional photovoltaic energy storage inverter according to claim 1, characterized in that, The S3 includes: S31, integrating the real-time hardware model parameters of S14 and the game decision result of S24, constructing a simulation input system containing 58 parameters; setting a simulation time step and a working condition scene label; S32, based on MATLAB / Simulink, building a coupling simulation platform of hardware structure and energy flow, calling a dynamic hardware topology structure model in real time during simulation, calculating the influence of hardware module switching on energy transmission path, substituting the energy game decision of each intelligent agent, simulating the dynamic flow process of energy among photovoltaic- energy storage-load-grid; introducing a random disturbance factor, simulating the system response under extreme working conditions; S33, collecting key parameters in the simulation process, performing time sequence segmentation on the collected data, calculating statistical characteristics of each period, constructing a device energy transmission simulation database, and associating corresponding hardware topology states and game decision parameters.

8. The method for controlling the operating state of a bidirectional photovoltaic energy storage inverter according to claim 1, characterized in that, The S4 includes: S41, obtaining running demand data of the bidirectional photovoltaic energy storage inverter through a system control center, and quantifying the demand data into an evaluatable index threshold; S42, based on the energy game decision result of S24 and the transmission simulation data of S33, a running state evaluation model is constructed: the weight of each demand index is determined by the analytic hierarchy process, the deviation function of the actual value and the threshold value of the index is established, and the single index compliance rate and the comprehensive compliance index are calculated; S43, the simulation data is substituted into the evaluation model to calculate the comprehensive compliance index under the current running state: if the comprehensive compliance index is greater than or equal to 0.9, it is determined that the running demand is met; if it is less than 0.9, the unqualified index is located, and its correlation with the hardware topology structure and the energy game strategy is analyzed; S44, for the unqualified running demand, the required energy adjustment amount is back calculated based on the deviation function, and each adjustment amount is integrated into the running demand energy evaluation data.

9. The method for controlling the operating state of a bidirectional photovoltaic energy storage inverter according to claim 1, characterized in that, The S5 comprises: S51, based on the device energy transmission simulation data of S33 and the running demand energy evaluation data of S44, an initial adjustment strategy is generated by using a fuzzy control algorithm; S52, combined with the dynamic hardware topology reconfigurable characteristics of S13, the selected strategy is optimized, the hardware structure parameters and the energy interaction strategy are jointly optimized by using a genetic algorithm, and a constraint condition is introduced to ensure the feasibility of hardware switching and the safety of energy adjustment; the optimal collaborative optimization strategy is output; S53, the optimal collaborative optimization strategy is simulated and verified on a real-time hardware model, the hardware state change and the energy flow response after the strategy execution are simulated, and whether all running demands are met is verified; potential risks are analyzed and warned, and if there is a risk, the strategy is returned to S51 to be regenerated; S54, the verified collaborative optimization strategy is converted into a standardized control instruction, which is transmitted to the control unit of the bidirectional photovoltaic energy storage inverter through industrial Ethernet; the control unit analyzes the instruction, drives the hardware module to perform the reconfiguration operation, and issues the energy interaction correction instruction to each intelligent agent; the strategy execution effect is monitored in real time to form a closed loop control.

10. A running state control system of a bidirectional photovoltaic energy storage inverter, comprising: one or more processors; a memory for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1 to 9.

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